# Divide, Cache, Conquer: Dichotomic Prompting for Efficient Multi-Label LLM-Based Classification

arXiv: https://arxiv.org/abs/2511.03830 · HTML: https://mlsanity.com/paper/2511.03830/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 48 references that are in the map.
- First posted on arXiv 5 Nov 2025.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [A Survey on Knowledge Distillation of Large Language Models](https://mlsanity.com/paper/2402.13116/) (2024)
- [You Only Cache Once: Decoder-Decoder Architectures for Language Models](https://mlsanity.com/paper/2405.05254/) (2024)
- [KLEJ: Comprehensive Benchmark for Polish Language Understanding](https://mlsanity.com/paper/2005.00630/) (2020)
- [HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish](https://mlsanity.com/paper/2105.01735/) (2021)
- [A Comprehensive Survey on Knowledge Distillation](https://mlsanity.com/paper/2503.12067/) (2025)
- [An Exploration of Encoder-Decoder Approaches to Multi-Label Classification for Legal and Biomedical Text](https://mlsanity.com/paper/2305.05627/) (2023)
- [Deep Learning for Multi-Label Learning: A Comprehensive Survey](https://mlsanity.com/paper/2401.16549/) (2024)
- [Deep Emotions Across Languages: A Novel Approach for Sentiment Propagation in Multilingual WordNets](https://mlsanity.com/paper/2312.04715/) (2023)
- [Backtranslation and paraphrasing in the LLM era? Comparing data augmentation methods for emotion classification](https://mlsanity.com/paper/2507.14590/) (2025)
- [Predicting stock prices with ChatGPT-annotated Reddit sentiment](https://mlsanity.com/paper/2507.22922/) (2025)

## Built on by (most cited first, 1 of 1)

- [What properties of reasoning supervision are associated with improved downstream model quality?](https://mlsanity.com/paper/2605.13290/) (2026)

## Abstract (arXiv)

We introduce a method for efficient multi-label text classification with large language models (LLMs), built on reformulating classification tasks as sequences of dichotomic (yes/no) decisions. Instead of generating all labels in a single structured response, each target dimension is queried independently, which, combined with a prefix caching mechanism, yields substantial efficiency gains for short-text inference without loss of accuracy. To demonstrate the approach, we focus on affective text analysis, covering 24 dimensions including emotions and sentiment. Using LLM-to-SLM distillation, a powerful annotator model (DeepSeek-V3) provides multiple annotations per text, which are aggregated to fine-tune smaller models (HerBERT-Large, CLARIN-1B, PLLuM-8B, Gemma3-1B). The fine-tuned models show significant improvements over zero-shot baselines, particularly on the dimensions seen during training. Our findings suggest that decomposing multi-label classification into dichotomic queries, combined with distillation and cache-aware inference, offers a scalable and effective framework for LLM-based classification. While we validate the method on affective states, the approach is general and applicable across domains.